Machine Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers.
Adalimumab
/ therapeutic use
Antirheumatic Agents
/ therapeutic use
Arthritis, Rheumatoid
/ drug therapy
Certolizumab Pegol
/ therapeutic use
Etanercept
/ therapeutic use
Female
Genetic Markers
/ drug effects
Humans
Infliximab
/ therapeutic use
Machine Learning
Male
Methotrexate
/ therapeutic use
Middle Aged
Normal Distribution
Outcome Assessment, Health Care
/ methods
Polymorphism, Single Nucleotide
Predictive Value of Tests
Regression Analysis
Reproducibility of Results
Severity of Illness Index
Treatment Outcome
Tumor Necrosis Factor-alpha
/ antagonists & inhibitors
Journal
Arthritis & rheumatology (Hoboken, N.J.)
ISSN: 2326-5205
Titre abrégé: Arthritis Rheumatol
Pays: United States
ID NLM: 101623795
Informations de publication
Date de publication:
12 2019
12 2019
Historique:
received:
21
05
2018
accepted:
18
07
2019
pubmed:
26
7
2019
medline:
10
3
2020
entrez:
26
7
2019
Statut:
ppublish
Résumé
Accurate prediction of treatment responses in rheumatoid arthritis (RA) patients can provide valuable information on effective drug selection. Anti-tumor necrosis factor (anti-TNF) drugs are an important second-line treatment after methotrexate, the classic first-line treatment for RA. However, patient heterogeneity hinders identification of predictive biomarkers and accurate modeling of anti-TNF drug responses. This study was undertaken to investigate the usefulness of machine learning to assist in developing predictive models for treatment response. Using data on patient demographics, baseline disease assessment, treatment, and single-nucleotide polymorphism (SNP) array from the Dialogue on Reverse Engineering Assessment and Methods (DREAM): Rheumatoid Arthritis Responder Challenge, we created a Gaussian process regression model to predict changes in the Disease Activity Score in 28 joints (DAS28) for the patients and to classify them into either the responder or the nonresponder group. This model was developed and cross-validated using data from 1,892 RA patients. It was evaluated using an independent data set from 680 patients. We examined the effectiveness of the similarity modeling and the contribution of individual features. In the cross-validation tests, our method predicted changes in DAS28 (ΔDAS28), with a correlation coefficient of 0.405. It correctly classified responses from 78% of patients. In the independent test, this method achieved a Pearson's correlation coefficient of 0.393 in predicting ΔDAS28. Gaussian process regression effectively remapped the feature space and identified subpopulations that do not respond well to anti-TNF treatments. Genetic SNP biomarkers showed small contributions in the prediction when added to the clinical models. This was the best-performing model in the DREAM Challenge. The model described here shows promise in guiding treatment decisions in clinical practice, based primarily on clinical profiles with additional genetic information.
Substances chimiques
Antirheumatic Agents
0
Genetic Markers
0
Tumor Necrosis Factor-alpha
0
Infliximab
B72HH48FLU
Adalimumab
FYS6T7F842
Etanercept
OP401G7OJC
Certolizumab Pegol
UMD07X179E
Methotrexate
YL5FZ2Y5U1
Types de publication
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1987-1996Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2019, American College of Rheumatology.
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